Machine Learning of Stem Cell Identities From Single-Cell Expression Data via Regulatory Network Archetypes.

ABSTRACT: The molecular regulatory network underlying stem cell pluripotency has been intensively studied, and we now have a reliable ensemble model for the "average" pluripotent cell. However, evidence of significant cell-to-cell variability suggests that the activity of this network varies within individual stem cells, leading to differential processing of environmental signals and variability in cell fates. Here, we adapt a method originally designed for face recognition to infer regulatory network patterns within individual cells from single-cell expression data. Using this method we identify three distinct network configurations in cultured mouse embryonic stem cells-corresponding to naïve and formative pluripotent states and an early primitive endoderm state-and associate these configurations

SUBMITTER: Stumpf PS 

PROVIDER: S-EPMC6349820 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature